class_docstr: Class for transition based parser. Implement 2 algorithms which are "arc-standard" and "arc-eager"
docstr: :param features: list of feature string which is needed to convert to binary features
  :type features: list(str)
  :return : string of binary features in libsvm format  which is 'featureID:value' pairs
docstr_tok: [":","param","features",":","list","of","feature","string","which","is","needed","to","convert","to","binary","features",":","type","features",":","list","(","str",")",":","return",":","string","of","binary","features","in","libsvm","format","which","is","'featureID",":","value","'","pairs"]
code: def _convert_to_binary_features(self, features):
      """
      :param features: list of feature string which is needed to convert to binary features
      :type features: list(str)
      :return : string of binary features in libsvm format  which is 'featureID:value' pairs
      """
      unsorted_result = []
      for feature in features:
          self._dictionary.setdefault(feature, len(self._dictionary))
          unsorted_result.append(self._dictionary[feature])
  
      # Default value of each feature is 1.0
      return " ".join(
          str(featureID) + ":1.0"
          for featureID in sorted(unsorted_result)
      )
  
imports: import pickle
  import tempfile
  from copy import deepcopy
  from operator import itemgetter
  from os import remove
  from nltk.parse import DependencyEvaluator, DependencyGraph, ParserI
label_desc: Use image node_id 3 for calling the TransitionParser obj's underlying member method code with example usage: obj._convert_to_binary_features(features) and returns: str